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2019 international conference on machine learning and data engineering, icmlde 2019

ISBN: (纸本)9781728104041

The proceedings contain 17 papers. The topics discussed include: spark-based machine learning pipeline construction method;implementation of chinese reader aid for visually-impaired by using neural network and text summarization technologies;a new percentage of sales method for forecasting additional funds needed;an artificially intelligent wearable device for dementia patients;development of IoT-based safety management method through an analysis of structural characteristics and risk factors for industrial valves;analysis of machine learning techniques for credit card fraud detection;social content mining in social networks;using knowledge discovery techniques to support tutoring in an open world intelligent game-based learning environment;and a clustering approach for outliers detection in a big point-of-sales database.

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Review of "Integrated Computer-Aided Process engineering Session in the 17th international Symposium on Novel and Nano Materials (ISNNM, 14-18 November 2022)"

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MATERIALS TRANSACTIONS 2025年第1期66卷 144-150页

作者： Lee, Yeon-Joo Cha, Pil-Ryung Kim, Hyoung-Seop Choi, Hyun-Joo Kookmin Univ Dept Mat Sci & Engn Seoul 02707 South Korea Pohang Univ Sci & Technol POSTECH Grad Inst Ferrous & Eco Mat Technol Pohang 37673 South Korea

"The 17th international Symposium on Novel and Nano Materials (ISNNM)" was held in Jeju, Korea from 14th to 18th November, 2022, and the proceedings for the session of "Integrated Computer-Aided Process engineering (ICAPE) were published in Feb, 2023, as a special issue of Materials Transactions (Vol. 64, No. 9). Following the first special issue, which covered the content of the ICAPE session at the international Symposium on Innovation in Materials Processing (ISIMP), this second special issue also presents various topics, including computational materials science, data-driven optimization, as well as experimental validation of optimized process. This article offers a concise overview of several key topics presented at the second special issue, including: macro-scale numerical analysis through finite element methods (FEM), and microstructure simulations using phase-field modelling (PFM), as well as various optimization methods such as machine learning (ML), artificial intelligence (AI), and design of experiments (DoE).

关键词： Integrated Computer-Aided Process engineering Computational Materials Simulations Design of Experiments Finite Element Methods Phase-Field Modelling

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A framework for tool wear and remaining useful life prediction in machining process using monitoring data and machine learning

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international JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第2期138卷 491-509页

作者： Lee, Soomin Jo, Wonkeun Koo, Hogeun Kwak, Juheon Kim, Hyein Koo, Jeongin Kim, Dongil Chungnam Natl Univ Dept Comp Sci & Engn 99 Daehak Ro Daejeon 34134 South Korea Ewha Womans Univ Div Artificial Intelligence & Software 52 Ewhayeodaegil Seoul 03760 South Korea Korea Inst Ind Technol Ind Transformat Technol Dept 89 Yangdaegiro Gil Cheonan 31056 South Korea Ewha Womans Univ Dept Data Sci 52 Ewhayeodaegil Seoul 03760 South Korea

Machining is an important type of manufacturing process. Because machining utilizes a tool to cut raw materials, tool fatigue is the main cause of degradation in productivity and efficiency. Hence, tool wear and remaining useful life (RUL), which is related to tool fatigue, should be managed to optimize the machining process. In this study, a framework for tool wear and RUL prediction using monitoring data and machine learning methods is proposed. First, real-world machining data are collected from sensors in the machines through a data acquisition system. Next, feature engineering, including time series feature extraction and correlation coefficient-based feature selection, is employed to construct a concise set of important features from the raw sensor data. Subsequently, tool wear is predicted using machine learning-based regression methods. Finally, RUL is predicted using iterative piecewise linear regression as real-time forecasting, which allows adaptation to changes in tool wear progression patterns and decision thresholds that frequently occur in real-world machining process. The experimental results reveal that (1) the proposed method outperformed the benchmark methods, (2) important features were selected using feature engineering, and (3) the proposed method, used without cutting force data, showed comparable results. Furthermore, we examined the flexibility of the proposed method with an example of a safe RUL prediction by manipulating the decision threshold to reduce the probability of tool failure.

关键词： Machining process Tool wear prediction Remaining useful life machine learning Feature engineering

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Predicting surface roughness in machining aluminum alloys taking into account material properties

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international JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2025年第4期38卷 555-576页

作者： Nguyen, Van-Hai Le, Tien-Thinh PHENIKAA Univ Fac Mech Engn & Mechatron Hanoi Vietnam A&A Green Phoenix Grp JSC PHENIKAA Res & Technol Inst PRATI Hanoi Vietnam

This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse milling dataset encompassing material properties and cutting parameters from various aluminum alloy grades was compiled from research articles. Four machine learning algorithms, Extreme Gradient Boosting (XGB), Random Forest (RFR), Catalogical Gradient Boosting (CAT), and Gradient Boosting Regression (GBR), were employed to develop the predictive model. The dataset underwent cleaning, imputation, and outlier removal to ensure data quality. Feature engineering incorporated material properties and cutting parameters for model training. Performance metrics such as RMSE, MAPE, and R2 were used to assess the models' accuracy. The SHapley Additive exPlanations (SHAP) technique was employed to interpret the models and identify influential features. GBR achieved the highest prediction accuracy with an RMSE of 0.2507 mu m, MAPE of 23.36%, and R2 of 0.8709. Thermal conductivity, feed rate, and cutting speed were consistently identified as the most influential factors, although their rankings differed slightly. This study successfully developed a GBR model for effective Ra prediction in aluminum alloy milling, supporting advancements in smart manufacturing by enabling accurate surface quality prediction and data-driven process optimization through machine learning.

关键词： Surface roughness End milling Multi-grades aluminum alloys machine learning Material properties

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Per-example gradient regularization improves learning signals from noisy data

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machine learning 2025年第3期114卷 1-20页

作者： Meng, Xuran Cao, Yuan Zou, Difan Umich Dept Biostat Ann Arbor MI 48109 USA HKU Dept Stat & Actuarial Sci Hong Kong 999077 Peoples R China HKU Inst Data Sci Dept Comp Sci Hong Kong 999077 Peoples R China

Gradient regularization, as described in Barrett and Dherin (in: international conference on learning representations, 2021), is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests that this regularization technique can significantly enhance the robustness of deep learning models against noisy perturbations, while also reducing test error. In this paper, we explore the per-example gradient regularization (PEGR) and present a theoretical analysis that demonstrates its effectiveness in improving both test error and robustness against noise perturbations. Specifically, we adopt a signal-noise data model from Cao et al. (Adv Neural Inf Process Syst 35:25237-25250, 2022) and show that PEGR can learn signals effectively while suppressing noise memorization. In contrast, standard gradient descent struggles to distinguish the signal from the noise, leading to suboptimal generalization performance. Our analysis reveals that PEGR penalizes the variance of pattern learning, thus effectively suppressing the memorization of noises from the training data. These findings underscore the importance of variance control in deep learning training and offer useful insights for developing more effective training approaches.

关键词： Gradient regularization Noise perturbations Variance control

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Understanding the local structure and thermophysical behavior of Mg-La liquid alloys via machine learning potential

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international Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 439-449页

作者： Jia Zhao Taixi Feng Guimin Lu National Engineering Research Center for Integrated Utilization of Salt Lake Resource East China University of Science and TechnologyShanghai 200237China Joint International Laboratory for Potassium and Lithium Strategic Resources East China University of Science and TechnologyShanghai 200237China

The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.

关键词： magnesium-lanthanum liquid alloys local structure macroscopic properties thermodynamic behavior deep potential mo-lecular dynamic simulation

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Multi-modal machine learning in Gastrointestinal Endoscopy: A Review 24

Multi-modal Machine Learning in Gastrointestinal Endoscopy: ...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Chan, In Neng Wong, Pak Kin Yan, Tao Hu, Yanyan Chan, Chon In Department of Electromechanical Engineering University of Macau China Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Xiangyang China Department of Gastroenterology Kiang Wu Hospital China School of Mechanical Engineering Hubei University of Arts and Science. China

ISBN: (纸本)9798400718274

The gastrointestinal (GI) tract plays a crucial role in the human body but is frequently affected by diseases that pose a significant global health issue. GI endoscopy is the primary diagnostic tool for early disease detection. However, its efficacy is limited by human factors such as fatigue and diagnostic variability. The integration of artificial intelligence (AI) in GI endoscopy, particularly through machine learning (ML) and deep learning (DL), offers a promising solution to enhance diagnostic accuracy and efficiency. Many studies have primarily focused on the sole use of endoscopic images, which limits the potential benefits of more comprehensive analysis with unimodal data. With advancements in DL algorithms that can handle complex data modalities, multi-modal machine learning (MMML) has emerged as an innovative approach that leverages multiple data modalities to enhance model performance by combining diverse information sources. Despite its potential, there is no specific review on the application of MMML in GI endoscopy. This paper reviews the applications of MMML in GI endoscopy and highlights its benefits in addressing the complexities of GI endoscopy tasks. Besides, it also explores the potential clinical applications and analyzes the challenges and provides recommendations for further advancements in this emerging field. Copyright © 2024 held by the owner/author(s).

关键词： Adversarial machine learning

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machine learning-aided hysteretic response prediction of double skin composite wall under earthquake loads

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JOURNAL OF BUILDING engineering 2025年 101卷

作者： Wang, Shiye Wang, Wei Wu, Yongtao Xie, Zhiyang Gao, Yuqing Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Ecole Polytech Fed Lausanne Sch Comp & Commun Sci CH-1015 Lausanne Switzerland Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China

Accurately and rapidly simulating the hysteretic behavior of double skin composite wall (DSCW) under earthquake loads enhance the efficiency of seismic performance assessments for high-rise steel-concrete composite structures. The application of artificial intelligence in civil engineering enables a shift from purely physics-based models to methods that integrate physical mechanisms with data-driven approaches. To this end, a machine learning-aided model for simulating the hysteretic behavior of DSCW under earthquake loads is proposed. Firstly, a fiber-based uniaxial material model incorporating softening was proposed to replicate the primary damaging mechanisms of DSCW, validated through comparison with experimental data. A comprehensive dataset covering a broad range of engineering metrics of DSCW was generated, incorporating various loading protocols. The modified Bouc-Wen model was utilized to calibrate hysteretic curves, solving an inverse problem via optimization techniques to determine consistent hysteretic parameters (CHPs) of DSCW. The analysis results confirm the highest accuracy of the artificial neural network (ANN) model. The SHapley Additive exPlanations (SHAP) analysis provided interpretability, highlighting the significant influence of geometric parameters and confirming the interdependence among Bouc-Wen model parameters. The performance of the ANN model was evaluated against a Transformer-based end-to-end deep learning model. The results indicated that the ANN model exhibited superior performance, particularly under asymmetric loading conditions, due to its integration of physical mechanisms into the force-displacement constitutive relationship.

关键词： Double skin composite wall machine learning Hysteretic response Consistent hysteretic parameter Earthquake loads

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learning from high-dimensional cyber-physical data streams: a case of large-scale smart grid

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international JOURNAL OF machine learning AND CYBERNETICS 2025年第3期16卷 1819-1831页

作者： Hassani, Hossein Hallaji, Ehsan Razavi-Far, Roozbeh Saif, Mehrdad Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada Univ New Brunswick Canadian Inst Cybersecur Fredericton NB E3B 5A3 Canada

Quality of data and complexity of decision boundaries in high-dimensional data streams that are collected from cyber-physical power systems can greatly influence the process of learning from data and diagnosing faults in such critical systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements that poses a challenge to the learning process, leading to a degradation in the performance of fault diagnosis. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in learning from data streams collected from cyber-physical systems. A data-driven fault diagnosis framework for a 118-bus power system is constructed by integrating feature selection, dimensionality reduction methods, and decision models. A comparative study is enabled accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis.

关键词： Classification Cyber-physical power systems Dimensionality reduction Fault diagnosis Feature selection High-dimensional data streams

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Automatic PDF Document Classification with machine learning 25th

Automatic PDF Document Classification with Machine Learning

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25th international conference on Intelligent data engineering and Automated learning

作者： Llacer Luna, Socrates Garigliotti, Dario Martinez Plumed, Fernando Ferri Ramirez, Cesar Univ Politecn Valencia Valencia Spain Univ Bergen Bergen Norway

ISBN: (纸本)9783031777301;9783031777318

Universitat Polit`ecnica de Val`encia (UPV) faces challenges in managing its Alfresco document repository, which contains 600,000 PDF files, of which only 100,000 are correctly categorised. Manual classification is laborious and error-prone, hindering information retrieval and advanced search capabilities. This project presents an automated pipeline that integrates optical character recognition (OCR) and machine learning to efficiently classify documents. Our approach distinguishes between scanned and digital documents, accurately extracts text and categorises it into 51 predefined categories using models such as BERT and RF. By improving document organisation and accessibility, this work optimises UPV's document management and paves the way for advanced search technologies and real-time classification systems.

关键词： Document Classification OCR machine learning Alfresco Repository

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